Overview
- Skill Key
- alirezarezvani/senior-data-engineer
- Author
- alirezarezvani
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/alirezarezvani/senior-data-engineer
- Latest Commit SHA
- 140c97f5be131477ea7f3f59e2b572b4e37e3495
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
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Status
ACTIVE
Visibility
PUBLIC
直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 senior-data-engineer 技能。 若已安装,则直接安装 senior-data-engineer 技能。
# Senior Data Engineer Production-grade data engineering skill for building scalable, reliable data systems. ## Table of Contents 1. [Trigger Phrases](#trigger-phrases) 2. [Quick Start](#quick-start) 3. [Workflows](#workflows) - [Building a Batch ETL Pipeline](#workflow-1-building-a-batch-etl-pipeline) - [Implementing Real-Time Streaming](#workflow-2-implementing-real-time-streaming) - [Data Quality Framework Setup](#workflow-3-data-quality-framework-setup) 4. [Architecture Decision Framework](#architecture-decision-framework) 5. [Tech Stack](#tech-stack) 6. [Reference Documentation](#reference-documentation) 7. [Troubleshooting](#troubleshooting) --- ## Trigger Phrases Activate this skill when you see: **Pipeline Design:** - "Design a data pipeline for..." - "Build an ETL/ELT process..." - "How should I ingest data from..." - "Set up data extraction from..." **Architecture:** - "Should I use batch or streaming?" - "Lambda vs Kappa architecture" - "How to handle late-arriving data" - "Design a data lakehouse" **Data Modeling:** - "Create a dimensional model..." - "Star schema vs snowflake" - "Implement slowly changing dimensions" - "Design a data vault" **Data Quality:** - "Add data validation to..." - "Set up data quality checks" - "Monitor data freshness" - "Implement data contracts" **Performance:** - "Optimize this Spark job" - "Query is running slow" - "Reduce pipeline execution time" - "Tune Airflow DAG" --- ## Quick Start ### Core Tools ```bash # Generate pipeline orchestration config python scripts/pipeline_orchestrator.py generate \ --type airflow \ --source postgres \ --destination snowflake \ --schedule "0 5 * * *" # Validate data quality python scripts/data_quality_validator.py validate \ --input data/sales.parquet \ --schema schemas/sales.json \ --checks freshness,completeness,uniqueness # Optimize ETL performance python scripts/etl_performance_optimizer.py analyze \ --query queries/daily_aggregation.sql \ --e...
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